Earth - Jasmine #10
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Earth - Jasmine #10
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CheezItMan
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May 1, 2021
CheezItMan
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Nice work Jasmine, you hit the learning goals here. Well done.
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| # Time Complexity: O(n * log(n)) where n is the number of elements. | ||
| # Space Complexity: O(log(n)) worsecase because of recursion inside of heapify? | ||
| # list = [5, 27, 3, 16, -50] | ||
| def heapsort(list) |
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👍 Really nice. However you're using a lot of one letter variable names.
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| # Time Complexity: O(log(n)) | ||
| # Space Complexity: O(log(n) due to the stack call in heap_up, could be O(1) using a loop | ||
| def add(key, value = key) |
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| # Time Complexity: O(log(n)) - where n is the number of elements and log(n) reps the levels | ||
| # Space Complexity: O(log(n) due to the stack call in heap_down, could be O(1) using a loop | ||
| def remove() |
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| # Time complexity: O(1) | ||
| # Space complexity: O(1) | ||
| def empty? |
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| # Time complexity: O(log(n)) - n for number of elements. log(n) represents the number of levels. | ||
| # Space complexity: O(log(n)) - the stack for the resursive calls could be as tall as the number of levels. | ||
| def heap_up(index) |
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| # This helper method takes an index and | ||
| # moves it up the heap if it's smaller | ||
| # than it's parent node. | ||
| def heap_down(index) |
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Heaps Practice
Congratulations! You're submitting your assignment!
Comprehension Questions
heap_up&heap_downmethods useful? Why?